Bacterial communities can act as precise biosensors of environmental damage

A multidisciplinary group of US-based researchers has shown that the mixture of species found within natural bacterial communities in the environment can accurately predict the presence of contaminants such as uranium, nitrate, and oil. The findings, published this week in mBio, the online open-access journal of the American Society for Microbiology, show that the rapid sequencing of microbiomes in place at environmental sites can be used to monitor damage caused by human activity.

"This approach might be a general way for us to see anthropogenic effects on the environment," says Terry Hazen, a microbial ecologist at University of Tennessee in Knoxville and Oak Ridge National Laboratory. "It's a way of finding out who's on first? Who is there in the bacterial community that can act as a biosensor and also as potential bioremediators?"

The team hypothesized that because bacteria are already continuously monitoring and responding to changing environmental conditions, then perhaps they could act as an onsite environmental surveillance network. Tracking the make-up of those bacterial communities could be digitized through DNA sequencing.

To find out if bacterial community structure was predictive of contamination, the team employed a method called supervised machine learning, using the Random forest algorithm that relies on an ensemble of thousands of decision trees. For their training set, the team sampled 93 different monitoring wells near Oak Ridge, some of which are known to be contaminated with uranium and nitrate from the early development of nuclear weapons at the site. Teams led by postdoctoral researcher Andrea Rocha took pains to collect the groundwater samples in the same way from each well over a period of three months.

From each sample, the microbial 16S rRNA gene was extracted and sequenced, yielding a total of 26,943 unique species of bacteria. After weeding out the species in very low abundance or narrowly distributed across the well sites, the team was left with 2,972 species to use as 'features' in the machine-learning model.

Then they asked if those features could be used to predict contamination. Using the DNA data, the model could accurately predict uranium contamination of the groundwater 88% of the time and nitrate contamination 73% of the time.

Next, the team wanted to know if their model could work for an altogether different environmental site and contaminant--oil from the Deepwater Horizon spill in the Gulf of Mexico. Using about 60 samples that Hazen's team had previously collected both before and during the 2010 oil spill, the model accurately predicted oil contamination 98% of the time.

Even more impressive, the model could predict the presence of previous contamination in samples where the chemical signature of oil hydrocarbons was absent. In other words, even after the oil had been degraded by bacteria, the presence of a particular bacterial community structure could still identify that the contamination event had taken place.

"This approach may be a sort of forensic tool also," says Hazen. "Even if we cannot detect a contaminant, but we see a community structure that has this predictive ability, then we know there must have been a leak or a spill or people disposing of something in the recent past." The researchers found the approach worked for predicting other geochemical characteristics like pH or the presence of manganese and aluminum as well.

"Today, if I want to know if water is safe to drink, whether it's contaminated with acidity or uranium, it's easier to just go in and measure it directly," explains Mark Smith, research director at OpenBiome in Medford, Massachusetts and former graduate student at Massachusetts Institute of Technology who led the computational analysis. "But we've found these microbial signatures that can persist beyond the direct measurements and that tell us how the bacteria are interacting with their environment." And that, he says, offers another advantage--to help researchers identify bacteria that are enriched at particular sites, which may be involved in the degradation or bioremediation of contaminants.

Source: American Society for Microbiology